Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations33600
Missing cells91479
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory184.0 B

Variable types

Text13
Numeric8
Categorical2

Alerts

wins has constant value "0" Constant
budget is highly overall correlated with grossWorldWide and 2 other fieldsHigh correlation
grossWorldWide is highly overall correlated with budget and 2 other fieldsHigh correlation
gross_US_Canada is highly overall correlated with budget and 2 other fieldsHigh correlation
opening_weekend_Gross is highly overall correlated with budget and 2 other fieldsHigh correlation
MPA has 7976 (23.7%) missing values Missing
budget has 21785 (64.8%) missing values Missing
grossWorldWide has 15378 (45.8%) missing values Missing
gross_US_Canada has 16029 (47.7%) missing values Missing
opening_weekend_Gross has 18077 (53.8%) missing values Missing
directors has 359 (1.1%) missing values Missing
writers has 1576 (4.7%) missing values Missing
stars has 473 (1.4%) missing values Missing
genres has 382 (1.1%) missing values Missing
countries_origin has 366 (1.1%) missing values Missing
filming_locations has 6729 (20.0%) missing values Missing
production_companies has 1378 (4.1%) missing values Missing
Languages has 474 (1.4%) missing values Missing
budget is highly skewed (γ1 = 97.63614372) Skewed
id has unique values Unique
Movie Link has unique values Unique
nominations has 23453 (69.8%) zeros Zeros
oscars has 31503 (93.8%) zeros Zeros

Reproduction

Analysis started2025-01-15 14:55:50.471094
Analysis finished2025-01-15 14:56:15.704285
Duration25.23 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

id
Text

Unique 

Distinct33600
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:16.156351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0647917
Min length9

Characters and Unicode

Total characters304577
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33600 ?
Unique (%)100.0%

Sample

1st rowtt0073195
2nd rowtt0073629
3rd rowtt0073486
4th rowtt0072890
5th rowtt0073692
ValueCountFrequency (%)
tt0073195 1
 
< 0.1%
tt0072976 1
 
< 0.1%
tt0072890 1
 
< 0.1%
tt0073692 1
 
< 0.1%
tt0072081 1
 
< 0.1%
tt0073026 1
 
< 0.1%
tt0072653 1
 
< 0.1%
tt0073812 1
 
< 0.1%
tt0073802 1
 
< 0.1%
tt0073317 1
 
< 0.1%
Other values (33590) 33590
> 99.9%
2025-01-15T20:26:17.145408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 67200
22.1%
0 56288
18.5%
1 27140
8.9%
2 21158
 
6.9%
6 20539
 
6.7%
8 19768
 
6.5%
7 18918
 
6.2%
4 18615
 
6.1%
5 18557
 
6.1%
3 18415
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237377
77.9%
Lowercase Letter 67200
 
22.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56288
23.7%
1 27140
11.4%
2 21158
 
8.9%
6 20539
 
8.7%
8 19768
 
8.3%
7 18918
 
8.0%
4 18615
 
7.8%
5 18557
 
7.8%
3 18415
 
7.8%
9 17979
 
7.6%
Lowercase Letter
ValueCountFrequency (%)
t 67200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 237377
77.9%
Latin 67200
 
22.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56288
23.7%
1 27140
11.4%
2 21158
 
8.9%
6 20539
 
8.7%
8 19768
 
8.3%
7 18918
 
8.0%
4 18615
 
7.8%
5 18557
 
7.8%
3 18415
 
7.8%
9 17979
 
7.6%
Latin
ValueCountFrequency (%)
t 67200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 67200
22.1%
0 56288
18.5%
1 27140
8.9%
2 21158
 
6.9%
6 20539
 
6.7%
8 19768
 
6.5%
7 18918
 
6.2%
4 18615
 
6.1%
5 18557
 
6.1%
3 18415
 
6.0%

Title
Text

Distinct31935
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:18.020365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length165
Median length74
Mean length16.206399
Min length1

Characters and Unicode

Total characters544535
Distinct characters132
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30564 ?
Unique (%)91.0%

Sample

1st rowJaws
2nd rowThe Rocky Horror Picture Show
3rd rowOne Flew Over the Cuckoo's Nest
4th rowDog Day Afternoon
5th rowShampoo
ValueCountFrequency (%)
the 10109
 
10.3%
of 3405
 
3.5%
a 1540
 
1.6%
and 1122
 
1.1%
in 1112
 
1.1%
to 689
 
0.7%
love 530
 
0.5%
504
 
0.5%
for 405
 
0.4%
man 402
 
0.4%
Other values (21418) 78082
79.8%
2025-01-15T20:26:19.278201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64300
 
11.8%
e 54733
 
10.1%
a 37115
 
6.8%
o 32784
 
6.0%
n 29402
 
5.4%
i 28755
 
5.3%
r 28619
 
5.3%
t 26109
 
4.8%
s 21255
 
3.9%
h 20333
 
3.7%
Other values (122) 201130
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 387254
71.1%
Uppercase Letter 82617
 
15.2%
Space Separator 64300
 
11.8%
Other Punctuation 7034
 
1.3%
Decimal Number 2513
 
0.5%
Dash Punctuation 662
 
0.1%
Open Punctuation 56
 
< 0.1%
Close Punctuation 56
 
< 0.1%
Math Symbol 16
 
< 0.1%
Other Number 12
 
< 0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 54733
14.1%
a 37115
9.6%
o 32784
 
8.5%
n 29402
 
7.6%
i 28755
 
7.4%
r 28619
 
7.4%
t 26109
 
6.7%
s 21255
 
5.5%
h 20333
 
5.3%
l 19029
 
4.9%
Other values (44) 89120
23.0%
Uppercase Letter
ValueCountFrequency (%)
T 11074
 
13.4%
S 7427
 
9.0%
M 5600
 
6.8%
B 5472
 
6.6%
C 4906
 
5.9%
A 4562
 
5.5%
D 4510
 
5.5%
L 4218
 
5.1%
H 3633
 
4.4%
W 3508
 
4.2%
Other values (27) 27707
33.5%
Other Punctuation
ValueCountFrequency (%)
: 2009
28.6%
' 1761
25.0%
. 1314
18.7%
, 845
12.0%
! 482
 
6.9%
& 337
 
4.8%
? 175
 
2.5%
/ 56
 
0.8%
* 26
 
0.4%
¡ 8
 
0.1%
Other values (6) 21
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 587
23.4%
1 416
16.6%
0 397
15.8%
3 311
12.4%
9 166
 
6.6%
4 159
 
6.3%
7 148
 
5.9%
5 133
 
5.3%
8 99
 
3.9%
6 97
 
3.9%
Other Number
ValueCountFrequency (%)
½ 8
66.7%
³ 2
 
16.7%
² 2
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 51
91.1%
[ 5
 
8.9%
Close Punctuation
ValueCountFrequency (%)
) 51
91.1%
] 5
 
8.9%
Math Symbol
ValueCountFrequency (%)
+ 15
93.8%
= 1
 
6.2%
Space Separator
ValueCountFrequency (%)
64300
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 662
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 10
100.0%
Other Symbol
ValueCountFrequency (%)
° 3
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 469872
86.3%
Common 74663
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 54733
 
11.6%
a 37115
 
7.9%
o 32784
 
7.0%
n 29402
 
6.3%
i 28755
 
6.1%
r 28619
 
6.1%
t 26109
 
5.6%
s 21255
 
4.5%
h 20333
 
4.3%
l 19029
 
4.0%
Other values (82) 171738
36.5%
Common
ValueCountFrequency (%)
64300
86.1%
: 2009
 
2.7%
' 1761
 
2.4%
. 1314
 
1.8%
, 845
 
1.1%
- 662
 
0.9%
2 587
 
0.8%
! 482
 
0.6%
1 416
 
0.6%
0 397
 
0.5%
Other values (30) 1890
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543613
99.8%
None 922
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64300
 
11.8%
e 54733
 
10.1%
a 37115
 
6.8%
o 32784
 
6.0%
n 29402
 
5.4%
i 28755
 
5.3%
r 28619
 
5.3%
t 26109
 
4.8%
s 21255
 
3.9%
h 20333
 
3.7%
Other values (75) 200208
36.8%
None
ValueCountFrequency (%)
é 161
17.5%
ä 69
 
7.5%
ü 60
 
6.5%
ö 60
 
6.5%
á 59
 
6.4%
í 56
 
6.1%
è 50
 
5.4%
å 45
 
4.9%
ô 43
 
4.7%
ó 43
 
4.7%
Other values (37) 276
29.9%

Movie Link
Text

Unique 

Distinct33600
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:19.827298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length36.064792
Min length36

Characters and Unicode

Total characters1211777
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33600 ?
Unique (%)100.0%

Sample

1st rowhttps://www.imdb.com/title/tt0073195
2nd rowhttps://www.imdb.com/title/tt0073629
3rd rowhttps://www.imdb.com/title/tt0073486
4th rowhttps://www.imdb.com/title/tt0072890
5th rowhttps://www.imdb.com/title/tt0073692
ValueCountFrequency (%)
https://www.imdb.com/title/tt0073195 1
 
< 0.1%
https://www.imdb.com/title/tt0072976 1
 
< 0.1%
https://www.imdb.com/title/tt0072890 1
 
< 0.1%
https://www.imdb.com/title/tt0073692 1
 
< 0.1%
https://www.imdb.com/title/tt0072081 1
 
< 0.1%
https://www.imdb.com/title/tt0073026 1
 
< 0.1%
https://www.imdb.com/title/tt0072653 1
 
< 0.1%
https://www.imdb.com/title/tt0073812 1
 
< 0.1%
https://www.imdb.com/title/tt0073802 1
 
< 0.1%
https://www.imdb.com/title/tt0073317 1
 
< 0.1%
Other values (33590) 33590
> 99.9%
2025-01-15T20:26:20.474742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 201600
16.6%
/ 134400
 
11.1%
w 100800
 
8.3%
. 67200
 
5.5%
i 67200
 
5.5%
m 67200
 
5.5%
0 56288
 
4.6%
h 33600
 
2.8%
c 33600
 
2.8%
e 33600
 
2.8%
Other values (16) 416289
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 739200
61.0%
Decimal Number 237377
 
19.6%
Other Punctuation 235200
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 201600
27.3%
w 100800
13.6%
i 67200
 
9.1%
m 67200
 
9.1%
h 33600
 
4.5%
c 33600
 
4.5%
e 33600
 
4.5%
l 33600
 
4.5%
o 33600
 
4.5%
b 33600
 
4.5%
Other values (3) 100800
13.6%
Decimal Number
ValueCountFrequency (%)
0 56288
23.7%
1 27140
11.4%
2 21158
 
8.9%
6 20539
 
8.7%
8 19768
 
8.3%
7 18918
 
8.0%
4 18615
 
7.8%
5 18557
 
7.8%
3 18415
 
7.8%
9 17979
 
7.6%
Other Punctuation
ValueCountFrequency (%)
/ 134400
57.1%
. 67200
28.6%
: 33600
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 739200
61.0%
Common 472577
39.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 201600
27.3%
w 100800
13.6%
i 67200
 
9.1%
m 67200
 
9.1%
h 33600
 
4.5%
c 33600
 
4.5%
e 33600
 
4.5%
l 33600
 
4.5%
o 33600
 
4.5%
b 33600
 
4.5%
Other values (3) 100800
13.6%
Common
ValueCountFrequency (%)
/ 134400
28.4%
. 67200
14.2%
0 56288
11.9%
: 33600
 
7.1%
1 27140
 
5.7%
2 21158
 
4.5%
6 20539
 
4.3%
8 19768
 
4.2%
7 18918
 
4.0%
4 18615
 
3.9%
Other values (3) 54951
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1211777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 201600
16.6%
/ 134400
 
11.1%
w 100800
 
8.3%
. 67200
 
5.5%
i 67200
 
5.5%
m 67200
 
5.5%
0 56288
 
4.6%
h 33600
 
2.8%
c 33600
 
2.8%
e 33600
 
2.8%
Other values (16) 416289
34.4%

Year
Real number (ℝ)

Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992.3936
Minimum1960
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:20.726678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963
Q11976
median1993
Q32009
95-th percentile2022
Maximum2024
Range64
Interquartile range (IQR)33

Descriptive statistics

Standard deviation18.957395
Coefficient of variation (CV)0.0095148845
Kurtosis-1.2140287
Mean1992.3936
Median Absolute Deviation (MAD)16
Skewness-0.02639572
Sum66944426
Variance359.38283
MonotonicityNot monotonic
2025-01-15T20:26:21.008457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 600
 
1.8%
2008 600
 
1.8%
2020 600
 
1.8%
2024 600
 
1.8%
2004 599
 
1.8%
1960 598
 
1.8%
2013 551
 
1.6%
2019 550
 
1.6%
1987 550
 
1.6%
1965 550
 
1.6%
Other values (55) 27802
82.7%
ValueCountFrequency (%)
1960 598
1.8%
1961 501
1.5%
1962 501
1.5%
1963 500
1.5%
1964 500
1.5%
1965 550
1.6%
1966 499
1.5%
1967 500
1.5%
1968 500
1.5%
1969 500
1.5%
ValueCountFrequency (%)
2024 600
1.8%
2023 550
1.6%
2022 600
1.8%
2021 500
1.5%
2020 600
1.8%
2019 550
1.6%
2018 500
1.5%
2017 550
1.6%
2016 500
1.5%
2015 500
1.5%
Distinct230
Distinct (%)0.7%
Missing221
Missing (%)0.7%
Memory size262.6 KiB
2025-01-15T20:26:21.323273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.8641361
Min length1

Characters and Unicode

Total characters195739
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.2%

Sample

1st row2h 4m
2nd row1h 40m
3rd row2h 13m
4th row2h 5m
5th row1h 50m
ValueCountFrequency (%)
1h 27917
42.2%
2h 4981
 
7.5%
30m 1728
 
2.6%
35m 1244
 
1.9%
40m 1122
 
1.7%
33m 1074
 
1.6%
32m 1038
 
1.6%
38m 978
 
1.5%
31m 962
 
1.5%
36m 959
 
1.5%
Other values (67) 24091
36.4%
2025-01-15T20:26:21.866559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 33363
17.0%
h 33189
17.0%
m 32898
16.8%
32715
16.7%
2 14674
7.5%
3 13905
7.1%
4 10170
 
5.2%
5 8155
 
4.2%
0 4452
 
2.3%
8 3359
 
1.7%
Other values (9) 8859
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96918
49.5%
Lowercase Letter 66087
33.8%
Space Separator 32715
 
16.7%
Uppercase Letter 14
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33363
34.4%
2 14674
15.1%
3 13905
14.3%
4 10170
 
10.5%
5 8155
 
8.4%
0 4452
 
4.6%
8 3359
 
3.5%
7 3090
 
3.2%
6 2983
 
3.1%
9 2767
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
G 5
35.7%
P 4
28.6%
T 2
 
14.3%
V 2
 
14.3%
X 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
h 33189
50.2%
m 32898
49.8%
Space Separator
ValueCountFrequency (%)
32715
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129638
66.2%
Latin 66101
33.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33363
25.7%
32715
25.2%
2 14674
11.3%
3 13905
10.7%
4 10170
 
7.8%
5 8155
 
6.3%
0 4452
 
3.4%
8 3359
 
2.6%
7 3090
 
2.4%
6 2983
 
2.3%
Other values (2) 2772
 
2.1%
Latin
ValueCountFrequency (%)
h 33189
50.2%
m 32898
49.8%
G 5
 
< 0.1%
P 4
 
< 0.1%
T 2
 
< 0.1%
V 2
 
< 0.1%
X 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33363
17.0%
h 33189
17.0%
m 32898
16.8%
32715
16.7%
2 14674
7.5%
3 13905
7.1%
4 10170
 
5.2%
5 8155
 
4.2%
0 4452
 
2.3%
8 3359
 
1.7%
Other values (9) 8859
 
4.5%

MPA
Categorical

Missing 

Distinct26
Distinct (%)0.1%
Missing7976
Missing (%)23.7%
Memory size262.6 KiB
R
10099 
Not Rated
4518 
PG-13
3780 
PG
3473 
Approved
 
981
Other values (21)
2773 

Length

Max length9
Median length8
Mean length3.7382922
Min length1

Characters and Unicode

Total characters95790
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPG
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 10099
30.1%
Not Rated 4518
13.4%
PG-13 3780
 
11.2%
PG 3473
 
10.3%
Approved 981
 
2.9%
Unrated 905
 
2.7%
G 768
 
2.3%
TV-MA 264
 
0.8%
TV-14 194
 
0.6%
X 148
 
0.4%
Other values (16) 494
 
1.5%
(Missing) 7976
23.7%

Length

2025-01-15T20:26:22.136680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 10099
33.5%
not 4518
15.0%
rated 4518
15.0%
pg-13 3780
 
12.5%
pg 3473
 
11.5%
approved 981
 
3.3%
unrated 905
 
3.0%
g 768
 
2.5%
tv-ma 264
 
0.9%
tv-14 194
 
0.6%
Other values (17) 642
 
2.1%

Most occurring characters

ValueCountFrequency (%)
R 14617
15.3%
t 9941
10.4%
G 8362
 
8.7%
P 7551
 
7.9%
e 6406
 
6.7%
d 6406
 
6.7%
o 5499
 
5.7%
a 5425
 
5.7%
N 4582
 
4.8%
4518
 
4.7%
Other values (24) 22483
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39415
41.1%
Uppercase Letter 39166
40.9%
Decimal Number 8129
 
8.5%
Space Separator 4518
 
4.7%
Dash Punctuation 4505
 
4.7%
Other Punctuation 40
 
< 0.1%
Math Symbol 17
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 14617
37.3%
G 8362
21.4%
P 7551
19.3%
N 4582
 
11.7%
A 1247
 
3.2%
U 905
 
2.3%
V 659
 
1.7%
T 654
 
1.7%
M 355
 
0.9%
X 148
 
0.4%
Other values (4) 86
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
t 9941
25.2%
e 6406
16.3%
d 6406
16.3%
o 5499
14.0%
a 5425
13.8%
p 1962
 
5.0%
r 1886
 
4.8%
v 981
 
2.5%
n 905
 
2.3%
s 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 4058
49.9%
3 3786
46.6%
4 194
 
2.4%
7 78
 
1.0%
6 8
 
0.1%
8 5
 
0.1%
Space Separator
ValueCountFrequency (%)
4518
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4505
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%
Math Symbol
ValueCountFrequency (%)
+ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78581
82.0%
Common 17209
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 14617
18.6%
t 9941
12.7%
G 8362
10.6%
P 7551
9.6%
e 6406
8.2%
d 6406
8.2%
o 5499
 
7.0%
a 5425
 
6.9%
N 4582
 
5.8%
p 1962
 
2.5%
Other values (14) 7830
10.0%
Common
ValueCountFrequency (%)
4518
26.3%
- 4505
26.2%
1 4058
23.6%
3 3786
22.0%
4 194
 
1.1%
7 78
 
0.5%
/ 40
 
0.2%
+ 17
 
0.1%
6 8
 
< 0.1%
8 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 14617
15.3%
t 9941
10.4%
G 8362
 
8.7%
P 7551
 
7.9%
e 6406
 
6.7%
d 6406
 
6.7%
o 5499
 
5.7%
a 5425
 
5.7%
N 4582
 
4.8%
4518
 
4.7%
Other values (24) 22483
23.5%

Rating
Real number (ℝ)

Distinct86
Distinct (%)0.3%
Missing138
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.1551581
Minimum1.1
Maximum9.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:22.376946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile4.1
Q15.5
median6.3
Q37
95-th percentile7.8
Maximum9.6
Range8.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1460699
Coefficient of variation (CV)0.18619666
Kurtosis0.33014337
Mean6.1551581
Median Absolute Deviation (MAD)0.8
Skewness-0.56822661
Sum205963.9
Variance1.3134761
MonotonicityNot monotonic
2025-01-15T20:26:22.647609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4 1274
 
3.8%
6.6 1243
 
3.7%
6.7 1237
 
3.7%
6.5 1226
 
3.6%
6.3 1219
 
3.6%
6.2 1198
 
3.6%
7.1 1177
 
3.5%
6.1 1177
 
3.5%
6.8 1141
 
3.4%
7 1113
 
3.3%
Other values (76) 21457
63.9%
ValueCountFrequency (%)
1.1 1
 
< 0.1%
1.2 2
 
< 0.1%
1.3 5
< 0.1%
1.4 2
 
< 0.1%
1.5 7
< 0.1%
1.6 7
< 0.1%
1.7 7
< 0.1%
1.8 7
< 0.1%
1.9 8
< 0.1%
2 5
< 0.1%
ValueCountFrequency (%)
9.6 2
 
< 0.1%
9.5 2
 
< 0.1%
9.4 4
 
< 0.1%
9.3 5
 
< 0.1%
9.2 9
 
< 0.1%
9.1 5
 
< 0.1%
9 10
 
< 0.1%
8.9 22
0.1%
8.8 31
0.1%
8.7 33
0.1%

Votes
Text

Distinct1758
Distinct (%)5.3%
Missing138
Missing (%)0.4%
Memory size262.6 KiB
2025-01-15T20:26:23.287246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2959178
Min length1

Characters and Unicode

Total characters110288
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)0.7%

Sample

1st row683K
2nd row173K
3rd row1.1M
4th row279K
5th row15K
ValueCountFrequency (%)
1.1k 577
 
1.7%
1.2k 451
 
1.3%
1.3k 443
 
1.3%
1.4k 412
 
1.2%
1.5k 373
 
1.1%
1.6k 365
 
1.1%
11k 364
 
1.1%
1.7k 346
 
1.0%
1.9k 320
 
1.0%
1.8k 317
 
0.9%
Other values (1748) 29494
88.1%
2025-01-15T20:26:24.643184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 21150
19.2%
1 15050
13.6%
. 10847
9.8%
2 10794
9.8%
3 9104
8.3%
4 7871
 
7.1%
5 7092
 
6.4%
6 6827
 
6.2%
7 6276
 
5.7%
8 5966
 
5.4%
Other values (3) 9311
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78217
70.9%
Uppercase Letter 21224
 
19.2%
Other Punctuation 10847
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15050
19.2%
2 10794
13.8%
3 9104
11.6%
4 7871
10.1%
5 7092
9.1%
6 6827
8.7%
7 6276
8.0%
8 5966
 
7.6%
9 5770
 
7.4%
0 3467
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
K 21150
99.7%
M 74
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 10847
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89064
80.8%
Latin 21224
 
19.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15050
16.9%
. 10847
12.2%
2 10794
12.1%
3 9104
10.2%
4 7871
8.8%
5 7092
8.0%
6 6827
7.7%
7 6276
7.0%
8 5966
 
6.7%
9 5770
 
6.5%
Latin
ValueCountFrequency (%)
K 21150
99.7%
M 74
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 21150
19.2%
1 15050
13.6%
. 10847
9.8%
2 10794
9.8%
3 9104
8.3%
4 7871
 
7.1%
5 7092
 
6.4%
6 6827
 
6.2%
7 6276
 
5.7%
8 5966
 
5.4%
Other values (3) 9311
8.4%

budget
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct1140
Distinct (%)9.6%
Missing21785
Missing (%)64.8%
Infinite0
Infinite (%)0.0%
Mean84543197
Minimum1
Maximum3 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:25.009682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile130000
Q12000000
median9000000
Q327000000
95-th percentile1.15 × 108
Maximum3 × 1011
Range3 × 1011
Interquartile range (IQR)25000000

Descriptive statistics

Standard deviation2.866281 × 109
Coefficient of variation (CV)33.903154
Kurtosis10155.268
Mean84543197
Median Absolute Deviation (MAD)8193053
Skewness97.636144
Sum9.9887787 × 1011
Variance8.215567 × 1018
MonotonicityNot monotonic
2025-01-15T20:26:25.465894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000 357
 
1.1%
5000000 350
 
1.0%
20000000 342
 
1.0%
3000000 309
 
0.9%
15000000 304
 
0.9%
1000000 277
 
0.8%
30000000 272
 
0.8%
25000000 272
 
0.8%
2000000 269
 
0.8%
4000000 239
 
0.7%
Other values (1130) 8824
26.3%
(Missing) 21785
64.8%
ValueCountFrequency (%)
1 2
< 0.1%
4 1
< 0.1%
10 1
< 0.1%
20 1
< 0.1%
100 1
< 0.1%
220 1
< 0.1%
230 1
< 0.1%
260 1
< 0.1%
300 2
< 0.1%
400 1
< 0.1%
ValueCountFrequency (%)
3 × 10111
 
< 0.1%
3.5 × 10101
 
< 0.1%
3 × 10103
< 0.1%
2.8 × 10101
 
< 0.1%
2.4 × 10101
 
< 0.1%
1.9 × 10101
 
< 0.1%
1.5 × 10102
< 0.1%
1.22155 × 10101
 
< 0.1%
1.2 × 10102
< 0.1%
1 × 10102
< 0.1%

grossWorldWide
Real number (ℝ)

High correlation  Missing 

Distinct18033
Distinct (%)99.0%
Missing15378
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean38149613
Minimum1
Maximum2.923706 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:25.893169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7790.85
Q1158993.75
median2311544
Q320929309
95-th percentile1.9391393 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)20770315

Descriptive statistics

Standard deviation1.2101046 × 108
Coefficient of variation (CV)3.1719971
Kurtosis95.00837
Mean38149613
Median Absolute Deviation (MAD)2298895.5
Skewness7.7779851
Sum6.9516224 × 1011
Variance1.4643531 × 1016
MonotonicityNot monotonic
2025-01-15T20:26:26.179036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8148 13
 
< 0.1%
509 9
 
< 0.1%
97182 6
 
< 0.1%
14000000 4
 
< 0.1%
11000000 4
 
< 0.1%
3451 4
 
< 0.1%
18000 4
 
< 0.1%
1500000 3
 
< 0.1%
2970 3
 
< 0.1%
2735 3
 
< 0.1%
Other values (18023) 18169
54.1%
(Missing) 15378
45.8%
ValueCountFrequency (%)
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
27 1
< 0.1%
32 1
< 0.1%
34 1
< 0.1%
42 2
< 0.1%
52 2
< 0.1%
54 1
< 0.1%
ValueCountFrequency (%)
2923706026 1
< 0.1%
2799439100 1
< 0.1%
2320250281 1
< 0.1%
2264750694 1
< 0.1%
2071310218 1
< 0.1%
2052415039 1
< 0.1%
1952723719 1
< 0.1%
1698772985 1
< 0.1%
1671537444 1
< 0.1%
1662020819 1
< 0.1%

gross_US_Canada
Real number (ℝ)

High correlation  Missing 

Distinct17211
Distinct (%)98.0%
Missing16029
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean18082363
Minimum64
Maximum9.3666222 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:26.511976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile9192
Q186036.5
median909411
Q314051372
95-th percentile92050430
Maximum9.3666222 × 108
Range9.3666216 × 108
Interquartile range (IQR)13965336

Descriptive statistics

Standard deviation48531806
Coefficient of variation (CV)2.6839305
Kurtosis62.143079
Mean18082363
Median Absolute Deviation (MAD)897342
Skewness6.369239
Sum3.1772521 × 1011
Variance2.3553362 × 1015
MonotonicityNot monotonic
2025-01-15T20:26:26.896479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8144 18
 
0.1%
509 12
 
< 0.1%
5000000 9
 
< 0.1%
97182 9
 
< 0.1%
321875 7
 
< 0.1%
25000 6
 
< 0.1%
11000000 6
 
< 0.1%
22168 5
 
< 0.1%
2180000 5
 
< 0.1%
7500000 5
 
< 0.1%
Other values (17201) 17489
52.1%
(Missing) 16029
47.7%
ValueCountFrequency (%)
64 1
< 0.1%
80 1
< 0.1%
95 1
< 0.1%
153 1
< 0.1%
180 1
< 0.1%
211 1
< 0.1%
212 1
< 0.1%
256 1
< 0.1%
309 1
< 0.1%
347 1
< 0.1%
ValueCountFrequency (%)
936662225 1
< 0.1%
858373000 1
< 0.1%
814866759 1
< 0.1%
785221649 1
< 0.1%
718732821 1
< 0.1%
700426566 1
< 0.1%
684075767 1
< 0.1%
678815482 1
< 0.1%
674292608 1
< 0.1%
653406625 1
< 0.1%

opening_weekend_Gross
Real number (ℝ)

High correlation  Missing 

Distinct14751
Distinct (%)95.0%
Missing18077
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean5110081.8
Minimum11
Maximum3.5711501 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:27.271582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile2787
Q113996.5
median107536
Q33772558.5
95-th percentile25028754
Maximum3.5711501 × 108
Range3.57115 × 108
Interquartile range (IQR)3758562

Descriptive statistics

Standard deviation14883189
Coefficient of variation (CV)2.9125148
Kurtosis81.785598
Mean5110081.8
Median Absolute Deviation (MAD)104362
Skewness7.2594433
Sum7.93238 × 1010
Variance2.2150931 × 1014
MonotonicityNot monotonic
2025-01-15T20:26:27.581077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11623 18
 
0.1%
11206 18
 
0.1%
512000 10
 
< 0.1%
2482000 8
 
< 0.1%
11537 8
 
< 0.1%
6000 6
 
< 0.1%
7000 6
 
< 0.1%
6500 5
 
< 0.1%
15942 5
 
< 0.1%
8000 5
 
< 0.1%
Other values (14741) 15434
45.9%
(Missing) 18077
53.8%
ValueCountFrequency (%)
11 1
< 0.1%
30 1
< 0.1%
46 1
< 0.1%
80 1
< 0.1%
89 1
< 0.1%
92 1
< 0.1%
95 1
< 0.1%
107 1
< 0.1%
112 1
< 0.1%
141 1
< 0.1%
ValueCountFrequency (%)
357115007 1
< 0.1%
260138569 1
< 0.1%
257698183 1
< 0.1%
247966675 1
< 0.1%
220009584 1
< 0.1%
211435291 1
< 0.1%
208806270 1
< 0.1%
207438708 1
< 0.1%
202003951 1
< 0.1%
191770759 1
< 0.1%

directors
Text

Missing 

Distinct14520
Distinct (%)43.7%
Missing359
Missing (%)1.1%
Memory size262.6 KiB
2025-01-15T20:26:28.361331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length85
Median length75
Mean length19.012184
Min length6

Characters and Unicode

Total characters631984
Distinct characters101
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9335 ?
Unique (%)28.1%

Sample

1st row['Steven Spielberg']
2nd row['Jim Sharman']
3rd row['Milos Forman']
4th row['Sidney Lumet']
5th row['Hal Ashby']
ValueCountFrequency (%)
john 893
 
1.2%
michael 674
 
0.9%
david 648
 
0.9%
robert 625
 
0.8%
peter 489
 
0.6%
richard 424
 
0.6%
james 378
 
0.5%
paul 353
 
0.5%
de 271
 
0.4%
lee 265
 
0.3%
Other values (14951) 71174
93.4%
2025-01-15T20:26:29.552080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 72226
 
11.4%
a 43597
 
6.9%
42953
 
6.8%
e 41588
 
6.6%
r 33476
 
5.3%
[ 33241
 
5.3%
] 33241
 
5.3%
n 32396
 
5.1%
i 31625
 
5.0%
o 29137
 
4.6%
Other values (91) 238504
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365928
57.9%
Other Punctuation 77969
 
12.3%
Uppercase Letter 77658
 
12.3%
Space Separator 42953
 
6.8%
Open Punctuation 33241
 
5.3%
Close Punctuation 33241
 
5.3%
Dash Punctuation 993
 
0.2%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 43597
11.9%
e 41588
11.4%
r 33476
 
9.1%
n 32396
 
8.9%
i 31625
 
8.6%
o 29137
 
8.0%
l 22035
 
6.0%
s 17188
 
4.7%
t 15757
 
4.3%
h 14010
 
3.8%
Other values (43) 85119
23.3%
Uppercase Letter
ValueCountFrequency (%)
M 6629
 
8.5%
S 6572
 
8.5%
J 5920
 
7.6%
R 5300
 
6.8%
C 4971
 
6.4%
B 4912
 
6.3%
A 4861
 
6.3%
D 4250
 
5.5%
G 3891
 
5.0%
P 3767
 
4.9%
Other values (29) 26585
34.2%
Other Punctuation
ValueCountFrequency (%)
' 72226
92.6%
, 2978
 
3.8%
. 2297
 
2.9%
" 468
 
0.6%
Space Separator
ValueCountFrequency (%)
42953
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 33241
100.0%
Close Punctuation
ValueCountFrequency (%)
] 33241
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 993
100.0%
Decimal Number
ValueCountFrequency (%)
9 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 443586
70.2%
Common 188398
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 43597
 
9.8%
e 41588
 
9.4%
r 33476
 
7.5%
n 32396
 
7.3%
i 31625
 
7.1%
o 29137
 
6.6%
l 22035
 
5.0%
s 17188
 
3.9%
t 15757
 
3.6%
h 14010
 
3.2%
Other values (82) 162777
36.7%
Common
ValueCountFrequency (%)
' 72226
38.3%
42953
22.8%
[ 33241
17.6%
] 33241
17.6%
, 2978
 
1.6%
. 2297
 
1.2%
- 993
 
0.5%
" 468
 
0.2%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 629207
99.6%
None 2777
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 72226
 
11.5%
a 43597
 
6.9%
42953
 
6.8%
e 41588
 
6.6%
r 33476
 
5.3%
[ 33241
 
5.3%
] 33241
 
5.3%
n 32396
 
5.1%
i 31625
 
5.0%
o 29137
 
4.6%
Other values (51) 235727
37.5%
None
ValueCountFrequency (%)
é 696
25.1%
á 306
11.0%
ô 300
10.8%
ó 226
 
8.1%
í 212
 
7.6%
ö 151
 
5.4%
ú 120
 
4.3%
ç 85
 
3.1%
ü 84
 
3.0%
è 70
 
2.5%
Other values (30) 527
19.0%

writers
Text

Missing 

Distinct27123
Distinct (%)84.7%
Missing1576
Missing (%)4.7%
Memory size262.6 KiB
2025-01-15T20:26:30.404705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length86
Median length68
Mean length34.883556
Min length7

Characters and Unicode

Total characters1117111
Distinct characters109
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24654 ?
Unique (%)77.0%

Sample

1st row['Peter Benchley', 'Carl Gottlieb']
2nd row["Richard O'Brien", 'Jim Sharman']
3rd row['Lawrence Hauben', 'Bo Goldman', 'Ken Kesey']
4th row['Frank Pierson', 'P.F. Kluge', 'Thomas Moore']
5th row['Robert Towne', 'Warren Beatty']
ValueCountFrequency (%)
john 1441
 
1.1%
david 1273
 
0.9%
michael 1047
 
0.8%
robert 986
 
0.7%
james 702
 
0.5%
peter 622
 
0.5%
paul 621
 
0.5%
de 586
 
0.4%
william 575
 
0.4%
richard 570
 
0.4%
Other values (26171) 126263
93.7%
2025-01-15T20:26:31.619435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 126685
 
11.3%
102662
 
9.2%
a 79105
 
7.1%
e 73163
 
6.5%
r 59068
 
5.3%
n 57599
 
5.2%
i 56030
 
5.0%
o 50916
 
4.6%
l 39304
 
3.5%
[ 32024
 
2.9%
Other values (99) 440555
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 647019
57.9%
Other Punctuation 164008
 
14.7%
Uppercase Letter 137709
 
12.3%
Space Separator 102662
 
9.2%
Open Punctuation 32024
 
2.9%
Close Punctuation 32024
 
2.9%
Dash Punctuation 1662
 
0.1%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 79105
12.2%
e 73163
11.3%
r 59068
 
9.1%
n 57599
 
8.9%
i 56030
 
8.7%
o 50916
 
7.9%
l 39304
 
6.1%
s 30019
 
4.6%
t 28029
 
4.3%
h 24295
 
3.8%
Other values (47) 149491
23.1%
Uppercase Letter
ValueCountFrequency (%)
M 11764
 
8.5%
S 11556
 
8.4%
J 10279
 
7.5%
B 9074
 
6.6%
C 8909
 
6.5%
R 8607
 
6.3%
A 8597
 
6.2%
D 7632
 
5.5%
G 7154
 
5.2%
L 6550
 
4.8%
Other values (31) 47587
34.6%
Other Punctuation
ValueCountFrequency (%)
' 126685
77.2%
, 31547
 
19.2%
. 4778
 
2.9%
" 998
 
0.6%
Decimal Number
ValueCountFrequency (%)
8 1
33.3%
5 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
102662
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 32024
100.0%
Close Punctuation
ValueCountFrequency (%)
] 32024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 784728
70.2%
Common 332383
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 79105
 
10.1%
e 73163
 
9.3%
r 59068
 
7.5%
n 57599
 
7.3%
i 56030
 
7.1%
o 50916
 
6.5%
l 39304
 
5.0%
s 30019
 
3.8%
t 28029
 
3.6%
h 24295
 
3.1%
Other values (88) 287200
36.6%
Common
ValueCountFrequency (%)
' 126685
38.1%
102662
30.9%
[ 32024
 
9.6%
] 32024
 
9.6%
, 31547
 
9.5%
. 4778
 
1.4%
- 1662
 
0.5%
" 998
 
0.3%
8 1
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1112313
99.6%
None 4798
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 126685
 
11.4%
102662
 
9.2%
a 79105
 
7.1%
e 73163
 
6.6%
r 59068
 
5.3%
n 57599
 
5.2%
i 56030
 
5.0%
o 50916
 
4.6%
l 39304
 
3.5%
[ 32024
 
2.9%
Other values (53) 435757
39.2%
None
ValueCountFrequency (%)
é 1264
26.3%
á 578
12.0%
ô 470
 
9.8%
í 403
 
8.4%
ó 348
 
7.3%
ü 214
 
4.5%
ö 206
 
4.3%
è 200
 
4.2%
ç 146
 
3.0%
ú 145
 
3.0%
Other values (36) 824
17.2%

stars
Text

Missing 

Distinct32812
Distinct (%)99.0%
Missing473
Missing (%)1.4%
Memory size262.6 KiB
2025-01-15T20:26:32.472699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length136
Median length82
Mean length51.245238
Min length6

Characters and Unicode

Total characters1697601
Distinct characters115
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32579 ?
Unique (%)98.3%

Sample

1st row['Roy Scheider', 'Robert Shaw', 'Richard Dreyfuss']
2nd row['Tim Curry', 'Susan Sarandon', 'Barry Bostwick']
3rd row['Jack Nicholson', 'Louise Fletcher', 'Michael Berryman']
4th row['Al Pacino', 'John Cazale', 'Penelope Allen']
5th row['Warren Beatty', 'Julie Christie', 'Goldie Hawn']
ValueCountFrequency (%)
john 1565
 
0.8%
michael 1281
 
0.6%
robert 1032
 
0.5%
james 945
 
0.5%
david 921
 
0.5%
richard 817
 
0.4%
peter 775
 
0.4%
lee 654
 
0.3%
paul 590
 
0.3%
de 539
 
0.3%
Other values (34124) 193686
95.5%
2025-01-15T20:26:33.586380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 197037
 
11.6%
169678
 
10.0%
a 127567
 
7.5%
e 115659
 
6.8%
n 93235
 
5.5%
i 85608
 
5.0%
r 84999
 
5.0%
o 71937
 
4.2%
, 65762
 
3.9%
l 61100
 
3.6%
Other values (105) 625019
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 985861
58.1%
Other Punctuation 266472
 
15.7%
Uppercase Letter 206795
 
12.2%
Space Separator 169678
 
10.0%
Open Punctuation 33127
 
2.0%
Close Punctuation 33127
 
2.0%
Dash Punctuation 2478
 
0.1%
Decimal Number 63
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 127567
12.9%
e 115659
11.7%
n 93235
9.5%
i 85608
 
8.7%
r 84999
 
8.6%
o 71937
 
7.3%
l 61100
 
6.2%
s 44212
 
4.5%
t 43929
 
4.5%
h 36699
 
3.7%
Other values (48) 220916
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 18510
 
9.0%
S 16991
 
8.2%
B 15023
 
7.3%
C 14657
 
7.1%
J 14555
 
7.0%
A 13432
 
6.5%
R 12103
 
5.9%
D 11642
 
5.6%
L 10476
 
5.1%
K 9763
 
4.7%
Other values (29) 69643
33.7%
Other Punctuation
ValueCountFrequency (%)
' 197037
73.9%
, 65762
 
24.7%
. 1900
 
0.7%
" 1766
 
0.7%
! 5
 
< 0.1%
/ 1
 
< 0.1%
& 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 32
50.8%
3 11
 
17.5%
5 9
 
14.3%
2 4
 
6.3%
4 3
 
4.8%
1 2
 
3.2%
7 2
 
3.2%
Space Separator
ValueCountFrequency (%)
169678
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 33127
100.0%
Close Punctuation
ValueCountFrequency (%)
] 33127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1192656
70.3%
Common 504945
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 127567
 
10.7%
e 115659
 
9.7%
n 93235
 
7.8%
i 85608
 
7.2%
r 84999
 
7.1%
o 71937
 
6.0%
l 61100
 
5.1%
s 44212
 
3.7%
t 43929
 
3.7%
h 36699
 
3.1%
Other values (87) 427711
35.9%
Common
ValueCountFrequency (%)
' 197037
39.0%
169678
33.6%
, 65762
 
13.0%
[ 33127
 
6.6%
] 33127
 
6.6%
- 2478
 
0.5%
. 1900
 
0.4%
" 1766
 
0.3%
0 32
 
< 0.1%
3 11
 
< 0.1%
Other values (8) 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1690729
99.6%
None 6872
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 197037
 
11.7%
169678
 
10.0%
a 127567
 
7.5%
e 115659
 
6.8%
n 93235
 
5.5%
i 85608
 
5.1%
r 84999
 
5.0%
o 71937
 
4.3%
, 65762
 
3.9%
l 61100
 
3.6%
Other values (60) 618147
36.6%
None
ValueCountFrequency (%)
é 1735
25.2%
á 861
12.5%
ô 695
10.1%
í 587
 
8.5%
ü 361
 
5.3%
è 358
 
5.2%
ó 340
 
4.9%
ö 324
 
4.7%
ç 215
 
3.1%
ø 180
 
2.6%
Other values (35) 1216
17.7%

genres
Text

Missing 

Distinct8540
Distinct (%)25.7%
Missing382
Missing (%)1.1%
Memory size262.6 KiB
2025-01-15T20:26:34.167890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length196
Median length169
Mean length36.094888
Min length7

Characters and Unicode

Total characters1199000
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6502 ?
Unique (%)19.6%

Sample

1st row['Monster Horror', 'Sea Adventure', 'Survival', 'Adventure', 'Drama', 'Horror', 'Thriller']
2nd row['Dark Comedy', 'Raunchy Comedy', 'Rock Musical', 'Supernatural Horror', 'Comedy', 'Horror', 'Musical']
3rd row['Medical Drama', 'Psychological Drama', 'Drama']
4th row['Heist', 'True Crime', 'Biography', 'Crime', 'Drama', 'Thriller']
5th row['Satire', 'Comedy', 'Drama']
ValueCountFrequency (%)
drama 20235
17.0%
comedy 14079
 
11.8%
thriller 7805
 
6.6%
romance 7047
 
5.9%
horror 6096
 
5.1%
action 5838
 
4.9%
crime 5552
 
4.7%
adventure 4825
 
4.1%
fantasy 3101
 
2.6%
mystery 2981
 
2.5%
Other values (174) 41436
34.8%
2025-01-15T20:26:35.079363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 207946
17.3%
r 90172
 
7.5%
85777
 
7.2%
a 79147
 
6.6%
, 70755
 
5.9%
e 64753
 
5.4%
o 57734
 
4.8%
m 56082
 
4.7%
i 46611
 
3.9%
y 35514
 
3.0%
Other values (47) 404509
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 640803
53.4%
Other Punctuation 278893
23.3%
Uppercase Letter 122764
 
10.2%
Space Separator 85777
 
7.2%
Open Punctuation 33218
 
2.8%
Close Punctuation 33218
 
2.8%
Dash Punctuation 4327
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 90172
14.1%
a 79147
12.4%
e 64753
10.1%
o 57734
9.0%
m 56082
8.8%
i 46611
 
7.3%
y 35514
 
5.5%
n 34248
 
5.3%
t 31881
 
5.0%
c 27426
 
4.3%
Other values (16) 117235
18.3%
Uppercase Letter
ValueCountFrequency (%)
D 25639
20.9%
C 21206
17.3%
A 13963
11.4%
T 9830
 
8.0%
F 9048
 
7.4%
S 8923
 
7.3%
H 8461
 
6.9%
R 7786
 
6.3%
M 6089
 
5.0%
W 3111
 
2.5%
Other values (14) 8708
 
7.1%
Other Punctuation
ValueCountFrequency (%)
' 207946
74.6%
, 70755
 
25.4%
& 192
 
0.1%
Space Separator
ValueCountFrequency (%)
85777
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 33218
100.0%
Close Punctuation
ValueCountFrequency (%)
] 33218
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 763567
63.7%
Common 435433
36.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 90172
 
11.8%
a 79147
 
10.4%
e 64753
 
8.5%
o 57734
 
7.6%
m 56082
 
7.3%
i 46611
 
6.1%
y 35514
 
4.7%
n 34248
 
4.5%
t 31881
 
4.2%
c 27426
 
3.6%
Other values (40) 239999
31.4%
Common
ValueCountFrequency (%)
' 207946
47.8%
85777
19.7%
, 70755
 
16.2%
[ 33218
 
7.6%
] 33218
 
7.6%
- 4327
 
1.0%
& 192
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1198965
> 99.9%
None 35
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 207946
17.3%
r 90172
 
7.5%
85777
 
7.2%
a 79147
 
6.6%
, 70755
 
5.9%
e 64753
 
5.4%
o 57734
 
4.8%
m 56082
 
4.7%
i 46611
 
3.9%
y 35514
 
3.0%
Other values (46) 404474
33.7%
None
ValueCountFrequency (%)
ō 35
100.0%

countries_origin
Text

Missing 

Distinct2938
Distinct (%)8.8%
Missing366
Missing (%)1.1%
Memory size262.6 KiB
2025-01-15T20:26:35.563356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length187
Median length168
Mean length19.947704
Min length8

Characters and Unicode

Total characters662942
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2249 ?
Unique (%)6.8%

Sample

1st row['United States']
2nd row['United Kingdom', 'United States']
3rd row['United States']
4th row['United States']
5th row['United States']
ValueCountFrequency (%)
united 22733
30.9%
states 18250
24.8%
kingdom 4437
 
6.0%
france 3904
 
5.3%
italy 2947
 
4.0%
germany 2302
 
3.1%
canada 1811
 
2.5%
india 1771
 
2.4%
japan 1445
 
2.0%
spain 1125
 
1.5%
Other values (170) 12789
17.4%
2025-01-15T20:26:36.641737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 95785
14.4%
t 65886
 
9.9%
e 55217
 
8.3%
a 46919
 
7.1%
n 46031
 
6.9%
40280
 
6.1%
i 36352
 
5.5%
[ 33234
 
5.0%
] 33234
 
5.0%
d 32786
 
4.9%
Other values (47) 177218
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 372271
56.2%
Other Punctuation 110451
 
16.7%
Uppercase Letter 73472
 
11.1%
Space Separator 40280
 
6.1%
Open Punctuation 33234
 
5.0%
Close Punctuation 33234
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 65886
17.7%
e 55217
14.8%
a 46919
12.6%
n 46031
12.4%
i 36352
9.8%
d 32786
8.8%
s 21582
 
5.8%
r 10946
 
2.9%
o 9518
 
2.6%
m 8034
 
2.2%
Other values (16) 39000
10.5%
Uppercase Letter
ValueCountFrequency (%)
U 22985
31.3%
S 20933
28.5%
K 5496
 
7.5%
I 5359
 
7.3%
F 4053
 
5.5%
C 2766
 
3.8%
G 2547
 
3.5%
J 1467
 
2.0%
A 1178
 
1.6%
W 949
 
1.3%
Other values (15) 5739
 
7.8%
Other Punctuation
ValueCountFrequency (%)
' 95785
86.7%
, 14660
 
13.3%
" 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
40280
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 33234
100.0%
Close Punctuation
ValueCountFrequency (%)
] 33234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 445743
67.2%
Common 217199
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 65886
14.8%
e 55217
12.4%
a 46919
10.5%
n 46031
10.3%
i 36352
8.2%
d 32786
 
7.4%
U 22985
 
5.2%
s 21582
 
4.8%
S 20933
 
4.7%
r 10946
 
2.5%
Other values (41) 86106
19.3%
Common
ValueCountFrequency (%)
' 95785
44.1%
40280
18.5%
[ 33234
 
15.3%
] 33234
 
15.3%
, 14660
 
6.7%
" 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 662939
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 95785
14.4%
t 65886
 
9.9%
e 55217
 
8.3%
a 46919
 
7.1%
n 46031
 
6.9%
40280
 
6.1%
i 36352
 
5.5%
[ 33234
 
5.0%
] 33234
 
5.0%
d 32786
 
4.9%
Other values (46) 177215
26.7%
None
ValueCountFrequency (%)
ô 3
100.0%

filming_locations
Text

Missing 

Distinct12383
Distinct (%)46.1%
Missing6729
Missing (%)20.0%
Memory size262.6 KiB
2025-01-15T20:26:37.340582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length163
Median length112
Mean length39.55491
Min length6

Characters and Unicode

Total characters1062880
Distinct characters119
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9827 ?
Unique (%)36.6%

Sample

1st row["Water Street, Edgartown, Martha's Vineyard, Massachusetts, USA"]
2nd row['Oakley Court, Windsor Road, Oakley Green, Windsor, Berkshire, England, UK']
3rd row['Oregon State Mental Hospital - 2600 Center Street NE, Salem, Oregon, USA']
4th row['285 Prospect Park West, Brooklyn, New York City, New York, USA']
5th row['2270 Bowmont Drive, Beverly Hills, California, USA']
ValueCountFrequency (%)
usa 11760
 
8.7%
california 4613
 
3.4%
new 4164
 
3.1%
york 3213
 
2.4%
2495
 
1.8%
city 2186
 
1.6%
uk 2172
 
1.6%
los 2039
 
1.5%
angeles 2016
 
1.5%
england 1905
 
1.4%
Other values (14616) 98654
73.0%
2025-01-15T20:26:38.371965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108346
 
10.2%
a 82890
 
7.8%
, 60883
 
5.7%
e 60598
 
5.7%
n 56562
 
5.3%
' 53177
 
5.0%
i 53144
 
5.0%
o 52640
 
5.0%
r 47842
 
4.5%
l 39802
 
3.7%
Other values (109) 446996
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 612203
57.6%
Uppercase Letter 154361
 
14.5%
Other Punctuation 116825
 
11.0%
Space Separator 108346
 
10.2%
Close Punctuation 26873
 
2.5%
Open Punctuation 26873
 
2.5%
Decimal Number 12997
 
1.2%
Dash Punctuation 4399
 
0.4%
Modifier Symbol 2
 
< 0.1%
Other Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 82890
13.5%
e 60598
9.9%
n 56562
9.2%
i 53144
8.7%
o 52640
8.6%
r 47842
 
7.8%
l 39802
 
6.5%
t 35482
 
5.8%
s 30695
 
5.0%
d 21737
 
3.6%
Other values (46) 130811
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 23137
15.0%
A 19766
12.8%
C 16423
 
10.6%
U 14935
 
9.7%
M 7482
 
4.8%
B 6973
 
4.5%
N 6626
 
4.3%
L 6457
 
4.2%
P 5834
 
3.8%
I 4360
 
2.8%
Other values (26) 42368
27.4%
Decimal Number
ValueCountFrequency (%)
0 2705
20.8%
1 2506
19.3%
2 1598
12.3%
5 1284
9.9%
3 1113
8.6%
4 999
 
7.7%
6 806
 
6.2%
7 737
 
5.7%
8 638
 
4.9%
9 611
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 60883
52.1%
' 53177
45.5%
. 1262
 
1.1%
" 1214
 
1.0%
& 220
 
0.2%
/ 58
 
< 0.1%
# 5
 
< 0.1%
\ 4
 
< 0.1%
; 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 26872
> 99.9%
) 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 26872
> 99.9%
( 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
108346
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4399
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 766564
72.1%
Common 296316
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 82890
 
10.8%
e 60598
 
7.9%
n 56562
 
7.4%
i 53144
 
6.9%
o 52640
 
6.9%
r 47842
 
6.2%
l 39802
 
5.2%
t 35482
 
4.6%
s 30695
 
4.0%
S 23137
 
3.0%
Other values (82) 283772
37.0%
Common
ValueCountFrequency (%)
108346
36.6%
, 60883
20.5%
' 53177
17.9%
] 26872
 
9.1%
[ 26872
 
9.1%
- 4399
 
1.5%
0 2705
 
0.9%
1 2506
 
0.8%
2 1598
 
0.5%
5 1284
 
0.4%
Other values (17) 7674
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1059862
99.7%
None 3018
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108346
 
10.2%
a 82890
 
7.8%
, 60883
 
5.7%
e 60598
 
5.7%
n 56562
 
5.3%
' 53177
 
5.0%
i 53144
 
5.0%
o 52640
 
5.0%
r 47842
 
4.5%
l 39802
 
3.8%
Other values (68) 443978
41.9%
None
ValueCountFrequency (%)
é 810
26.8%
í 394
13.1%
ä 268
 
8.9%
á 179
 
5.9%
ó 177
 
5.9%
à 170
 
5.6%
ô 147
 
4.9%
è 145
 
4.8%
ö 115
 
3.8%
æ 85
 
2.8%
Other values (31) 528
17.5%

production_companies
Text

Missing 

Distinct25940
Distinct (%)80.5%
Missing1378
Missing (%)4.1%
Memory size262.6 KiB
2025-01-15T20:26:39.107149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length192
Median length123
Mean length47.035411
Min length6

Characters and Unicode

Total characters1515575
Distinct characters121
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23844 ?
Unique (%)74.0%

Sample

1st row['Zanuck/Brown Productions', 'Universal Pictures']
2nd row['Twentieth Century Fox', 'Michael White Productions']
3rd row['Fantasy Films', 'N.V. Zvaluw']
4th row['Warner Bros.', 'Artists Entertainment Complex']
5th row['Persky-Bright / Vista', 'Columbia Pictures', 'Rubeeker Films']
ValueCountFrequency (%)
productions 10018
 
6.0%
films 9926
 
5.9%
pictures 7651
 
4.6%
film 6423
 
3.8%
entertainment 4998
 
3.0%
company 1726
 
1.0%
international 1372
 
0.8%
the 1272
 
0.8%
media 1163
 
0.7%
production 1023
 
0.6%
Other values (19809) 122195
72.8%
2025-01-15T20:26:40.283600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135545
 
8.9%
' 133900
 
8.8%
i 103135
 
6.8%
e 88556
 
5.8%
n 86292
 
5.7%
o 79711
 
5.3%
t 78393
 
5.2%
r 76800
 
5.1%
a 74289
 
4.9%
s 60936
 
4.0%
Other values (111) 598018
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 937979
61.9%
Uppercase Letter 184884
 
12.2%
Other Punctuation 178523
 
11.8%
Space Separator 135545
 
8.9%
Open Punctuation 35821
 
2.4%
Close Punctuation 35821
 
2.4%
Decimal Number 3822
 
0.3%
Dash Punctuation 2821
 
0.2%
Math Symbol 344
 
< 0.1%
Other Symbol 6
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 103135
11.0%
e 88556
9.4%
n 86292
9.2%
o 79711
8.5%
t 78393
 
8.4%
r 76800
 
8.2%
a 74289
 
7.9%
s 60936
 
6.5%
l 48788
 
5.2%
m 43624
 
4.7%
Other values (43) 197455
21.1%
Uppercase Letter
ValueCountFrequency (%)
P 27120
14.7%
F 25931
14.0%
C 19227
 
10.4%
S 11508
 
6.2%
M 10302
 
5.6%
A 9867
 
5.3%
E 9345
 
5.1%
B 8051
 
4.4%
T 7606
 
4.1%
I 7415
 
4.0%
Other values (26) 48512
26.2%
Other Punctuation
ValueCountFrequency (%)
' 133900
75.0%
, 35131
 
19.7%
. 6571
 
3.7%
" 1332
 
0.7%
& 814
 
0.5%
/ 721
 
0.4%
! 35
 
< 0.1%
@ 7
 
< 0.1%
: 7
 
< 0.1%
? 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 891
23.3%
0 614
16.1%
1 596
15.6%
3 471
12.3%
4 400
10.5%
9 188
 
4.9%
7 187
 
4.9%
8 179
 
4.7%
5 157
 
4.1%
6 139
 
3.6%
Open Punctuation
ValueCountFrequency (%)
[ 32241
90.0%
( 3580
 
10.0%
Close Punctuation
ValueCountFrequency (%)
] 32241
90.0%
) 3580
 
10.0%
Space Separator
ValueCountFrequency (%)
135545
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2821
100.0%
Math Symbol
ValueCountFrequency (%)
+ 344
100.0%
Other Symbol
ValueCountFrequency (%)
° 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Number
ValueCountFrequency (%)
² 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1122863
74.1%
Common 392712
 
25.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 103135
 
9.2%
e 88556
 
7.9%
n 86292
 
7.7%
o 79711
 
7.1%
t 78393
 
7.0%
r 76800
 
6.8%
a 74289
 
6.6%
s 60936
 
5.4%
l 48788
 
4.3%
m 43624
 
3.9%
Other values (79) 382339
34.1%
Common
ValueCountFrequency (%)
135545
34.5%
' 133900
34.1%
, 35131
 
8.9%
[ 32241
 
8.2%
] 32241
 
8.2%
. 6571
 
1.7%
( 3580
 
0.9%
) 3580
 
0.9%
- 2821
 
0.7%
" 1332
 
0.3%
Other values (22) 5770
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1510769
99.7%
None 4806
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135545
 
9.0%
' 133900
 
8.9%
i 103135
 
6.8%
e 88556
 
5.9%
n 86292
 
5.7%
o 79711
 
5.3%
t 78393
 
5.2%
r 76800
 
5.1%
a 74289
 
4.9%
s 60936
 
4.0%
Other values (72) 593212
39.3%
None
ValueCountFrequency (%)
é 2664
55.4%
á 553
 
11.5%
ó 321
 
6.7%
í 173
 
3.6%
ç 169
 
3.5%
ü 155
 
3.2%
É 91
 
1.9%
è 84
 
1.7%
ú 73
 
1.5%
ñ 72
 
1.5%
Other values (29) 451
 
9.4%

Languages
Text

Missing 

Distinct2709
Distinct (%)8.2%
Missing474
Missing (%)1.4%
Memory size262.6 KiB
2025-01-15T20:26:40.958275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length203
Median length11
Mean length15.615498
Min length7

Characters and Unicode

Total characters517279
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2042 ?
Unique (%)6.2%

Sample

1st row['English']
2nd row['English']
3rd row['English']
4th row['English']
5th row['English']
ValueCountFrequency (%)
english 23409
49.0%
french 3786
 
7.9%
spanish 2832
 
5.9%
italian 2780
 
5.8%
german 2055
 
4.3%
japanese 1494
 
3.1%
hindi 1361
 
2.9%
russian 857
 
1.8%
mandarin 783
 
1.6%
cantonese 539
 
1.1%
Other values (241) 7850
 
16.4%
2025-01-15T20:26:42.015457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 94622
18.3%
n 45793
 
8.9%
i 39194
 
7.6%
[ 33126
 
6.4%
] 33126
 
6.4%
s 32549
 
6.3%
h 32319
 
6.2%
l 27685
 
5.4%
g 25077
 
4.8%
E 23446
 
4.5%
Other values (54) 130342
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 279495
54.0%
Other Punctuation 108826
 
21.0%
Uppercase Letter 47838
 
9.2%
Open Punctuation 33149
 
6.4%
Close Punctuation 33149
 
6.4%
Space Separator 14620
 
2.8%
Dash Punctuation 138
 
< 0.1%
Decimal Number 64
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 45793
16.4%
i 39194
14.0%
s 32549
11.6%
h 32319
11.6%
l 27685
9.9%
g 25077
9.0%
a 22397
8.0%
e 14772
 
5.3%
r 10260
 
3.7%
c 4932
 
1.8%
Other values (16) 24517
8.8%
Uppercase Letter
ValueCountFrequency (%)
E 23446
49.0%
F 4045
 
8.5%
S 3727
 
7.8%
I 2954
 
6.2%
G 2449
 
5.1%
H 1909
 
4.0%
J 1495
 
3.1%
C 1073
 
2.2%
R 1016
 
2.1%
M 975
 
2.0%
Other values (16) 4749
 
9.9%
Decimal Number
ValueCountFrequency (%)
1 16
25.0%
4 16
25.0%
5 16
25.0%
3 16
25.0%
Other Punctuation
ValueCountFrequency (%)
' 94622
86.9%
, 14204
 
13.1%
Open Punctuation
ValueCountFrequency (%)
[ 33126
99.9%
( 23
 
0.1%
Close Punctuation
ValueCountFrequency (%)
] 33126
99.9%
) 23
 
0.1%
Space Separator
ValueCountFrequency (%)
14620
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 327333
63.3%
Common 189946
36.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 45793
14.0%
i 39194
12.0%
s 32549
9.9%
h 32319
9.9%
l 27685
8.5%
g 25077
7.7%
E 23446
7.2%
a 22397
6.8%
e 14772
 
4.5%
r 10260
 
3.1%
Other values (42) 53841
16.4%
Common
ValueCountFrequency (%)
' 94622
49.8%
[ 33126
 
17.4%
] 33126
 
17.4%
14620
 
7.7%
, 14204
 
7.5%
- 138
 
0.1%
( 23
 
< 0.1%
) 23
 
< 0.1%
1 16
 
< 0.1%
4 16
 
< 0.1%
Other values (2) 32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 94622
18.3%
n 45793
 
8.9%
i 39194
 
7.6%
[ 33126
 
6.4%
] 33126
 
6.4%
s 32549
 
6.3%
h 32319
 
6.2%
l 27685
 
5.4%
g 25077
 
4.8%
E 23446
 
4.5%
Other values (54) 130342
25.2%

wins
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.6 KiB
0
33600 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33600
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 33600
100.0%

Length

2025-01-15T20:26:42.315875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-15T20:26:42.477422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 33600
100.0%

Most occurring characters

ValueCountFrequency (%)
0 33600
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33600
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33600
100.0%

nominations
Real number (ℝ)

Zeros 

Distinct220
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8503571
Minimum0
Maximum433
Zeros23453
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:42.666601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile23
Maximum433
Range433
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.719188
Coefficient of variation (CV)3.6531719
Kurtosis136.92517
Mean4.8503571
Median Absolute Deviation (MAD)0
Skewness9.7027983
Sum162972
Variance313.96963
MonotonicityNot monotonic
2025-01-15T20:26:42.949735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23453
69.8%
2 1305
 
3.9%
3 1029
 
3.1%
4 867
 
2.6%
5 776
 
2.3%
6 634
 
1.9%
7 544
 
1.6%
8 474
 
1.4%
9 403
 
1.2%
10 340
 
1.0%
Other values (210) 3775
 
11.2%
ValueCountFrequency (%)
0 23453
69.8%
2 1305
 
3.9%
3 1029
 
3.1%
4 867
 
2.6%
5 776
 
2.3%
6 634
 
1.9%
7 544
 
1.6%
8 474
 
1.4%
9 403
 
1.2%
10 340
 
1.0%
ValueCountFrequency (%)
433 1
< 0.1%
425 1
< 0.1%
414 1
< 0.1%
394 1
< 0.1%
382 1
< 0.1%
375 1
< 0.1%
369 1
< 0.1%
358 1
< 0.1%
350 1
< 0.1%
337 1
< 0.1%

oscars
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10261905
Minimum0
Maximum11
Zeros31503
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size262.6 KiB
2025-01-15T20:26:43.183261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50868728
Coefficient of variation (CV)4.9570454
Kurtosis91.503665
Mean0.10261905
Median Absolute Deviation (MAD)0
Skewness8.089619
Sum3448
Variance0.25876275
MonotonicityNot monotonic
2025-01-15T20:26:43.733153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 31503
93.8%
1 1422
 
4.2%
2 363
 
1.1%
3 145
 
0.4%
4 74
 
0.2%
5 45
 
0.1%
6 19
 
0.1%
7 17
 
0.1%
8 5
 
< 0.1%
10 4
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 31503
93.8%
1 1422
 
4.2%
2 363
 
1.1%
3 145
 
0.4%
4 74
 
0.2%
5 45
 
0.1%
6 19
 
0.1%
7 17
 
0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 4
 
< 0.1%
9 1
 
< 0.1%
8 5
 
< 0.1%
7 17
 
0.1%
6 19
 
0.1%
5 45
 
0.1%
4 74
 
0.2%
3 145
 
0.4%
2 363
1.1%

Interactions

2025-01-15T20:26:11.205395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:58.465787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:00.447736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:02.276866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:04.007860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:05.902216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:07.641485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:09.280006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:11.394392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:58.734781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:00.698961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:02.569596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:04.211638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:06.063171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:07.864600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:09.567432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:11.611922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:59.007610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:00.865326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:02.842954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:04.420479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:06.274670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:08.113704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:09.798844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:11.842803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:59.239647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:01.075631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:03.087248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:04.666491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:06.491833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:08.310726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:10.076715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:12.079552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:59.475063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:01.346518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:03.287068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:04.885956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:06.755073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:08.513158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:10.308442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:12.312903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:59.679259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:01.598404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:03.505449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:05.112659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:06.972341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:08.687630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:10.512248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:12.510886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:25:59.920580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:01.786288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:03.670918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:05.416041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:07.143635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:08.876916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:10.820096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:12.765735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:00.284933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:02.031820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:03.845814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:05.660512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:07.376879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:09.061564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-15T20:26:10.988519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-15T20:26:43.876697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
MPARatingYearbudgetgrossWorldWidegross_US_Canadanominationsopening_weekend_Grossoscars
MPA1.0000.0960.2710.0000.0560.0610.0220.0560.010
Rating0.0961.0000.1110.1040.0190.0270.412-0.1390.227
Year0.2710.1111.0000.395-0.001-0.1920.375-0.116-0.004
budget0.0000.1040.3951.0000.7060.6440.3290.6900.121
grossWorldWide0.0560.019-0.0010.7061.0000.9040.3190.8470.174
gross_US_Canada0.0610.027-0.1920.6440.9041.0000.2420.9240.207
nominations0.0220.4120.3750.3290.3190.2421.0000.1210.323
opening_weekend_Gross0.056-0.139-0.1160.6900.8470.9240.1211.0000.106
oscars0.0100.227-0.0040.1210.1740.2070.3230.1061.000

Missing values

2025-01-15T20:26:13.238434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-15T20:26:14.212439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-15T20:26:15.036242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idTitleMovie LinkYearDurationMPARatingVotesbudgetgrossWorldWidegross_US_Canadaopening_weekend_Grossdirectorswritersstarsgenrescountries_originfilming_locationsproduction_companiesLanguageswinsnominationsoscars
0tt0073195Jawshttps://www.imdb.com/title/tt007319519752h 4mPG8.1683K7000000.0477220580.0266567580.07061513.0['Steven Spielberg']['Peter Benchley', 'Carl Gottlieb']['Roy Scheider', 'Robert Shaw', 'Richard Dreyfuss']['Monster Horror', 'Sea Adventure', 'Survival', 'Adventure', 'Drama', 'Horror', 'Thriller']['United States']["Water Street, Edgartown, Martha's Vineyard, Massachusetts, USA"]['Zanuck/Brown Productions', 'Universal Pictures']['English']0200
1tt0073629The Rocky Horror Picture Showhttps://www.imdb.com/title/tt007362919751h 40mR7.4173K1200000.0115798478.0112892319.0NaN['Jim Sharman']["Richard O'Brien", 'Jim Sharman']['Tim Curry', 'Susan Sarandon', 'Barry Bostwick']['Dark Comedy', 'Raunchy Comedy', 'Rock Musical', 'Supernatural Horror', 'Comedy', 'Horror', 'Musical']['United Kingdom', 'United States']['Oakley Court, Windsor Road, Oakley Green, Windsor, Berkshire, England, UK']['Twentieth Century Fox', 'Michael White Productions']['English']040
2tt0073486One Flew Over the Cuckoo's Nesthttps://www.imdb.com/title/tt007348619752h 13mR8.71.1M3000000.0109115366.0108981275.0NaN['Milos Forman']['Lawrence Hauben', 'Bo Goldman', 'Ken Kesey']['Jack Nicholson', 'Louise Fletcher', 'Michael Berryman']['Medical Drama', 'Psychological Drama', 'Drama']['United States']['Oregon State Mental Hospital - 2600 Center Street NE, Salem, Oregon, USA']['Fantasy Films', 'N.V. Zvaluw']['English']0150
3tt0072890Dog Day Afternoonhttps://www.imdb.com/title/tt007289019752h 5mR8.0279K1800000.050002721.050000000.0NaN['Sidney Lumet']['Frank Pierson', 'P.F. Kluge', 'Thomas Moore']['Al Pacino', 'John Cazale', 'Penelope Allen']['Heist', 'True Crime', 'Biography', 'Crime', 'Drama', 'Thriller']['United States']['285 Prospect Park West, Brooklyn, New York City, New York, USA']['Warner Bros.', 'Artists Entertainment Complex']['English']0200
4tt0073692Shampoohttps://www.imdb.com/title/tt007369219751h 50mR6.415K4000000.049407734.049407734.0NaN['Hal Ashby']['Robert Towne', 'Warren Beatty']['Warren Beatty', 'Julie Christie', 'Goldie Hawn']['Satire', 'Comedy', 'Drama']['United States']['2270 Bowmont Drive, Beverly Hills, California, USA']['Persky-Bright / Vista', 'Columbia Pictures', 'Rubeeker Films']['English']0110
5tt0072081The Return of the Pink Pantherhttps://www.imdb.com/title/tt007208119751h 53mG7.031K5000000.041833423.041833347.0NaN['Blake Edwards']['Frank Waldman', 'Blake Edwards']['Peter Sellers', 'Christopher Plummer', 'Catherine Schell']['Farce', 'Slapstick', 'Comedy', 'Crime', 'Mystery']['United Kingdom', 'United States']['Palace Hotel, Gstaad, Switzerland']['ITC Films', 'Jewel Productions', 'Pimlico Films']['English']050
6tt0073026Funny Ladyhttps://www.imdb.com/title/tt007302619752h 16mPG6.26.1KNaN39000000.039000000.0NaN['Herbert Ross']['Jay Presson Allen', 'Arnold Schulman']['Barbra Streisand', 'James Caan', 'Omar Sharif']['Biography', 'Comedy', 'Drama', 'Musical', 'Romance']['United States']['Central Station, Oakland, California, USA']['Columbia Pictures', 'Rastar Pictures', 'Vista']['English']005
7tt0072653The Apple Dumpling Ganghttps://www.imdb.com/title/tt007265319751h 40mG6.46.7KNaN36853000.036853000.0NaN['Norman Tokar']['Don Tait', 'Jack M. Bickham']['Bill Bixby', 'Susan Clark', 'Don Knotts']['Slapstick', 'Comedy', 'Family', 'Western']['United States']['Bend, Oregon, USA']['Walt Disney Productions']['English']000
8tt0073812Tommyhttps://www.imdb.com/title/tt007381219751h 51mPG6.623K5000000.034279846.034251525.0NaN['Ken Russell']['The Who', 'Ken Russell', 'Pete Townshend']['Roger Daltrey', 'Ann-Margret', 'Oliver Reed']['Jukebox Musical', 'Rock Musical', 'Drama', 'Musical']['United Kingdom']['Kings Theatre, 20-24 Albert Road, Southsea, Portsmouth, Hampshire, England, UK']['Robert Stigwood Organisation Ltd.', 'Hemdale']['English']052
9tt0073802Three Days of the Condorhttps://www.imdb.com/title/tt007380219751h 57mR7.465K20000000.027476252.027476252.0NaN['Sydney Pollack']['James Grady', 'Lorenzo Semple Jr.', 'David Rayfiel']['Robert Redford', 'Faye Dunaway', 'Cliff Robertson']['Political Thriller', 'Spy', 'Crime', 'Mystery', 'Thriller']['United States']['55 East 77th Street, Manhattan, New York City, New York, USA']['Wildwood Enterprises', 'Dino De Laurentiis Company']['English', 'French']041
idTitleMovie LinkYearDurationMPARatingVotesbudgetgrossWorldWidegross_US_Canadaopening_weekend_Grossdirectorswritersstarsgenrescountries_originfilming_locationsproduction_companiesLanguageswinsnominationsoscars
33590tt0109809FleshEaterhttps://www.imdb.com/title/tt010980919881h 28mR4.92.1K60000.0NaNNaNNaN['S. William Hinzman']['S. William Hinzman', 'Bill Randolph']['S. William Hinzman', 'John Mowod', 'Leslie Ann Wick']['B-Horror', 'Body Horror', 'Horror']['United States']['Beaver Falls, Pennsylvania, USA']['H&G Films Ltd.', 'Hinzman']['English']000
33591tt0090582The Abominationhttps://www.imdb.com/title/tt009058219881h 29mNaN4.4770NaNNaNNaNNaN['Bret McCormick']['Bret McCormick']['Scott Davis', 'Jude Johnson', 'Blue Thompson']['Horror']['United States']['Poolville, Texas, USA']['Donna Michelle Productions']['English']000
33592tt0096876Bad Bloodhttps://www.imdb.com/title/tt009687619881h 44mR4.6199NaNNaNNaNNaN['Chuck Vincent']['Craig Horrall']['Georgina Spelvin', 'Randy Spears', 'Linda Blair']['Drama', 'Thriller']['United States']['New York City, New York, USA']['Platinum Pictures (II)']['English']000
33593tt0096084Shadows in the Stormhttps://www.imdb.com/title/tt009608419881h 25mR3.9264NaNNaNNaNNaN['Terrell Tannen']['Terrell Tannen']['Ned Beatty', 'Mia Sara', 'Michael Madsen']['Crime', 'Drama', 'Mystery', 'Romance', 'Thriller']['United States']['Camp Nelson, California, USA']NaN['English']000
33594tt0095177Freewayhttps://www.imdb.com/title/tt009517719881h 31mR5.1588NaNNaN142671.0NaN['Francis Delia']['Deanne Barkley', 'Francis Delia', 'Darrell Fetty']['Darlanne Fluegel', 'James Russo', 'Billy Drago']['Thriller']['United States']['Los Angeles, California, USA']['Gower Street Pictures']['English']000
33595tt0094076The Southhttps://www.imdb.com/title/tt009407619882h 7mR7.31.1KNaNNaNNaNNaN['Fernando E. Solanas']['Fernando E. Solanas']['Susú Pecoraro', 'Miguel Ángel Solá', 'Philippe Léotard']['Drama']['Argentina', 'France']['Buenos Aires, Federal District, Argentina']['Canal+', 'Cinesur (Envar El Kadri)', 'Productions Pacific']['Spanish', 'French']020
33596tt0256664El cabaretero y sus golfashttps://www.imdb.com/title/tt025666419881h 25mNaN4.912NaNNaNNaNNaN['Raúl Ramírez']['Raúl Marcelo']['Raúl Ramírez', 'Raúl Marcelo', 'Marcela Daviland']['Comedy']['Mexico']NaNNaN['Spanish']000
33597tt0353261BraveStarr: The Legendhttps://www.imdb.com/title/tt035326119881h 31mPG6.81.3KNaNNaNNaNNaN['Tom Tataranowicz']['Bob Forward', 'Steve Hayes']['Charlie Adler', 'Susan Blu', 'Pat Fraley']['Superhero', 'Action', 'Adventure', 'Animation', 'Comedy', 'Family', 'Fantasy', 'Sci-Fi', 'Western']['United States']NaN['Filmation Associates']['English']000
33598tt0098474Fighting Madam 2https://www.imdb.com/title/tt009847419881h 30mNaN6.3337NaNNaNNaNNaN['Teresa Woo']['William Hsu', 'Teresa Woo', 'Larry Dolgin']['Alex Fong', 'Moon Lee', 'Elaine Lui']['Action']['Hong Kong']['Kuala Lumpur, Malaysia']['Molesworth Limited']['Cantonese', 'Mandarin']000
33599tt0096039Saturday the 14th Strikes Backhttps://www.imdb.com/title/tt009603919881h 18mPG3.1737NaNNaNNaNNaN['Howard R. Cohen']['Howard R. Cohen']['Ray Walston', 'Avery Schreiber', 'Patty McCormack']['Comedy', 'Fantasy', 'Horror', 'Sci-Fi']['United States']['Venice, California, USA']['Pacific Trust']['English']000